It is estimated that while over 100,000 genes are expressed by a mammalian genome, only a fraction are expressed in any particular cell or tissue. Gene expression patterns, especially as reflected in the abundance of mRNAs, vary according to cell or tissue type, with developmental or metabolic state, in response to insult or injury, and as a consequence of other genetic and environmental factors. Moreover, the pattern of expression changes in a dynamic fashion over time with changes in cell state and environment. The term “transcriptome” has been coined to describe the set of all genes expressed, at any given time, under defined conditions in a given tissue (Velculescu et al., 1997, Cell 88:243-51).
The detection of changes to the transcriptome can provide useful information regarding the identity of genes and gene products important in development, drug response, and, particularly, human disease processes. However, methods now used for identifying changes in the transcriptome suffer from a variety of deficiencies, e.g., they are expensive, require relatively large quantities of starting material, and/or do not efficiently identify low abundance transcripts important in mediating cell processes.
While a change in the expression of a particular gene between different cell states is evidence that the gene may be responsible for the difference in cell states, it would be preferable that the putative role assigned to the gene be validated. Such validation ideally would involve an assay system in which one can interrogate what effect, if any, modulation of expression of the gene has on a cellular state or cellular activity. If modulation of expression was found to be correlated with a change in cellular state or activity, this would substantiate the putative role for the gene. Thus, there remains a need for high throughput methods for first identifying genes that appear to play a role in a particular cellular state or activity and then validating that the gene does in fact have such a role.